1. Theme 2 Types of Data

2. Overview Variables can be anything that vary This lecture considers:
Types of data
Different scales of measurement
The different functions of statistics
The need to understand the characteristics of each of your variables is emphasised

3. Types of Data Broadly speaking, there are two types of data
The first type is called ‘nominal’ data
Nominal variables are those in which a case falls into one of two or more categories
Examples are gender, eye colour, socio-economic status, marital status and nationality
Nominal variables are also referred to as categorical or qualitative variables

4. Types of Data (Continued)
The second type of data is called ‘numerical’ data
Numerical variables are those in which a case is assigned a numerical value
Examples are age, height, weight, IQ, test scores, income, distance or temperature
Numerical variables are also referred to as score or quantitative variables

5. Nominal or Numerical? Which type of data are each of the following?
Facebook user (i.e. yes or no)
Use of Facebook (i.e. hours per day)
Network (i.e. number of friends on Facebook)
How do you access Facebook (i.e. mobile phone, PC or laptop)
Use of Facebook (i.e. Less than five hours per day or at least five hours per day)

6. Scales of Measurement Four different scales of measurement exist:
Nominal categorisation – Placing cases into named categories (e.g. sprinters could be categorised based on their nationality)
Ordinal categorisation – This ranks cases based on their order on a given variable (i.e. sprinters can be ranked 1st, 2nd, 3rd etc.)
Interval categorisation – Where the distances between the sequential points on the scale are equal (e.g. the temperature at the time of the race)
Ratio categorisation – The same as interval categorisation, but with an absolute zero (e.g. the sprinters’ best times)

7. Measurement Characteristics
Figure 1.1 The different scales of measurement and their main characteristics

8. Functions of Statistical Techniques
Statistical techniques perform three key functions:
Descriptive statistics, as you would guess, is used to describe the information collected
Inferential statistics relates to the confidence with which we can generalise from our sample to the population of interest
Data reduction techniques allow a researcher to make sense of large amounts of data through using more advanced statistics

9. Conclusions Data comes in two broad forms
Nominal variables are those in which cases are placed in categories
Numerical variables concern those in which cases are given a score value
It is important to understand which of these types of data each of your variables are as this will influence your analysis
The measurement of a variable can take four forms: nominal, ordinal, interval and ratio
Statistics can be used to describe, make inferences about or reduce your data

10. City
Country
Population
London
England
7,556,900
Birmingham
984,333
Liverpool
864,122
Nottingham
729,977
Sheffield
685,368
Bristol
617,280
Glasgow
Scotland
591,620
Leicester
508,916
Edinburgh
464,990
Leeds
455,123

11. Decade °C °F
1880s
13.73
56.71
1890s
13.75
56.74
1900s
13.74
56.73
1910s
13.72
56.70
1920s
13.83
56.89
1930s
13.96
57.12
1940s
14.04
57.26
1950s
13.98
57.16
1960s
13.99
57.18
1970s
14.00
57.20
1980s
14.18
57.52
1990s
14.31
57.76
2000s
14.51
58.12